Image enhancement methods, apparatus, electronic devices and storage media

Abstract

This application relates to an image enhancement method, apparatus, electronic device, and storage medium. The method includes: receiving and decoding an input signal from an image playback device to obtain an image to be displayed; dividing the image to be displayed according to a preset partitioning rule to obtain a set of sub-images corresponding to backlight partitions; simultaneously calculating the initial brightness of each sub-image in the sub-image set to obtain an initial partition brightness data table; obtaining partition correction data for each sub-image in image enhancement processing based on the data table to obtain a partition brightness correction data table; and controlling the LED array illumination brightness of the corresponding backlight partition of the image playback device according to the table to achieve image enhancement. By dividing the image to be displayed into multiple sub-images corresponding to backlight partitions and performing partition brightness statistics, combined with a three-level progressive image enhancement processing of dark areas, sharpness, and motion, the image display's sense of depth, clarity, and dynamic display quality are significantly improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image enhancement method, apparatus, electronic device and storage medium. Background Technology

[0002] With the widespread application of image playback devices such as LCD and MiniLED displays, users' demands for dynamic image display quality, dark scene detail reproduction, and clarity in motion scenes are constantly increasing. To improve display effects, existing image enhancement technologies typically perform uniform brightness adjustment, sharpening, or motion compensation on the entire image, combined with backlight control to optimize the overall display effect. Some existing solutions have adopted a zoned backlight control strategy, dividing the display screen into multiple areas corresponding to backlight zones, and achieving localized light control by statistically analyzing the brightness of each area, thus improving image contrast and display depth to a certain extent.

[0003] However, existing image enhancement and backlight control solutions still have significant shortcomings. Traditional image enhancement often adopts a global, uniform enhancement approach, which cannot perform differentiated and refined processing on dark areas, edge details, and moving areas of the image. This easily leads to problems such as loss of dark details, blurred edges, or motion blur. At the same time, brightness statistics and enhancement correction processes are independent of each other, and the zonal brightness data is not adjusted synchronously according to the actual processing effects of dark area enhancement, sharpness enhancement, and motion enhancement, resulting in a mismatch between backlight control and image enhancement. In addition, multi-level enhancement processing lacks a unified step-by-step execution logic. Each enhancement stage is independent and fails to form a collaborative optimization, making it prone to brightness distortion in moving areas and insufficient overall image depth and clarity, which is difficult to meet the usage requirements of high dynamic range display scenarios.

[0004] Therefore, there is an urgent need to develop an image enhancement method, device, electronic device, and storage medium to solve one or more of the aforementioned problems. Summary of the Invention

[0005] In view of this, to solve the above-mentioned technical problems or some of the technical problems, embodiments of the present invention provide an image enhancement method, apparatus, electronic device, and storage medium. The method divides the image to be displayed into a set of sub-images corresponding one-to-one with the backlight partitions of the image playback device. Initial partition brightness statistics are performed in parallel on each sub-image to obtain an initial partition brightness data table. Based on this, for each sub-image, corresponding correction parameters are determined in multi-level image enhancement processing, including shadow correction values, sharpness correction values, and motion correction values, in addition to shadow correction values, sharpness correction values, and motion correction values. Shadow enhancement processing is performed sequentially to optimize the shadow brightness of the sub-images; sharpness enhancement processing is performed to improve the edge clarity of the sub-images; and motion enhancement processing is performed to compensate for brightness loss in moving areas. Using the initial partition brightness data table as a reference, synchronous step-by-step corrections are performed based on the aforementioned correction values ​​for each sub-image, thereby obtaining a partition brightness correction data table that matches the enhanced image. By controlling the LED array brightness of the corresponding backlight zone of the image playback device according to the brightness correction data table of the zone, the precise coordination between image enhancement and zone backlight control is achieved. This effectively solves the problems of loss of dark details, edge blurring, motion blur and mismatch between backlight control and image enhancement caused by global uniform enhancement in traditional solutions. It significantly improves the display quality of dynamic images, the restoration of dark field details and the clarity of motion scenes, and meets the usage requirements of high dynamic display scenarios.

[0006] In a first aspect, this application provides an image enhancement method, the method comprising: Receive an input signal from an image playback device, and perform decoding processing on the input signal to obtain an image to be displayed; The image to be displayed is divided into multiple sub-images that correspond one-to-one with the backlight partitions of the image playback device according to the preset partitioning rules, thus obtaining a set of sub-images. Perform initial brightness statistics of each sub-image in the sub-image set in parallel to obtain an initial partition brightness data table including the original brightness information of each backlight partition; Based on the initial partition brightness data table, the partition correction data generated by each sub-image in the sub-image set during the image enhancement process is obtained, resulting in a partition brightness correction data table including the brightness information of each backlight partition after correction. The brightness of the LED array in the corresponding backlight zone of the image playback device is controlled according to the partition brightness correction data table to achieve image enhancement.

[0007] In one possible implementation, the step of obtaining partition correction data for each sub-image in the sub-image set during image enhancement processing based on the initial partition brightness data table, to obtain a partition brightness correction data table including brightness information of each backlight partition after correction, includes: Determine the correction parameters corresponding to each sub-image in each level of enhancement processing, and perform multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters. The correction parameters include shadow correction value, sharpness correction value and motion correction value, and the multi-level image enhancement processing includes shadow enhancement processing, sharpness enhancement processing and motion enhancement processing. Based on the initial partition brightness data table, synchronous step-by-step corrections are made based on the dark area correction value, sharpness correction value, and motion correction value of each sub-image to obtain a partition brightness correction data table that matches the enhanced image.

[0008] In one possible implementation, determining the correction parameters corresponding to each sub-image in the shadow enhancement process includes: Determine in turn whether there are dark areas in each sub-image; For any sub-image containing dark areas, determine the maximum set brightness value for the dark areas; Based on the maximum set brightness value, determine the target pixel brightness of the dark area; The dark correction value of the sub-image is determined based on the original pixel brightness before the dark area enhancement and the target pixel brightness.

[0009] In one possible implementation, determining whether a dark region exists in a sub-image includes: Calculate the average pixel brightness of all pixels in the sub-image; If the average pixel brightness is less than the dark area determination brightness threshold, it is determined that there is a dark area in the sub-image; If the average pixel brightness is greater than or equal to the dark area determination brightness threshold, it is determined that there is no dark area in the sub-image.

[0010] In one possible implementation, determining the correction parameters corresponding to each sub-image in the sharpness enhancement process includes: For any sub-image, perform grayscale processing on the sub-image, and perform Fourier transform on the grayscale image to obtain the image spectrum function of the sub-image; The image spectrum function is compared with a preset sharpness cutoff frequency to determine the sharpness region of the sub-image, wherein the frequency of the sharpness region is greater than the preset sharpness cutoff frequency; The spectrum of the sharpness region is enhanced based on a preset sharpening intensity coefficient and a high-pass filter function, and the pixel brightness of the sub-image after sharpening is obtained by inverse Fourier transform. The sharpness correction value of the sub-image is determined based on the original pixel brightness before sharpness enhancement and the pixel brightness after sharpness enhancement in the sub-image sharpness region.

[0011] In one possible implementation, determining the correction parameters corresponding to each sub-image in motion enhancement processing includes: For any sub-image, based on the pixel difference between the sub-image and the adjacent previous frame image, the motion region in the sub-image is identified; The motion enhancement coefficient of the sub-image is determined based on the motion amplitude of the motion region; The pixel brightness of the moving region is compensated and adjusted based on the motion enhancement coefficient to obtain the motion-enhanced pixel brightness; The motion correction value of the sub-image is determined based on the original pixel brightness before motion enhancement and the pixel brightness after motion enhancement of the sub-image motion region.

[0012] In one possible implementation, the step of sequentially performing multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters includes: For any sub-image, extract the dark correction value corresponding to the dark enhancement processing from the correction parameters, and perform dark enhancement processing on the dark area of ​​the sub-image based on the dark correction value to complete the dark brightness optimization of the sub-image; After the dark area enhancement processing is completed, the sharpness correction value corresponding to the sharpness enhancement processing is extracted from the correction parameters, and the sharpness enhancement processing is performed on the sharpness area of ​​the sub-image based on the sharpness correction value to improve the edge clarity of the sub-image. After the sharpness enhancement process is completed, the motion correction value corresponding to the motion enhancement process is extracted from the correction parameters. Based on the motion correction value, motion enhancement processing is performed on the motion region of the sub-image to compensate for the brightness loss of the motion region.

[0013] Secondly, this application provides an image enhancement apparatus, the apparatus comprising: The receiving module is used to receive input signals from the image playback device and perform decoding processing on the input signals to obtain the image to be displayed; The partitioning module is used to divide the image to be displayed into multiple sub-images that correspond one-to-one with the backlight partitions of the image playback device according to a preset partitioning rule, thereby obtaining a set of sub-images. The statistics module is used to perform initial brightness statistics of each sub-image in the sub-image set in parallel to obtain an initial partition brightness data table including the original brightness information of each backlight partition; The acquisition module is used to acquire the partition correction data generated by each sub-image in the sub-image set during the image enhancement process based on the initial partition brightness data table, and obtain a partition brightness correction data table including the brightness information of each backlight partition after correction. The control module is used to control the brightness of the LED array in the corresponding backlight zone of the image playback device according to the partition brightness correction data table, so as to achieve image enhancement.

[0014] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the image enhancement method described in any embodiment of the first aspect.

[0015] Fourthly, this application also provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the image enhancement method described in any embodiment of the first aspect.

[0016] Compared with the prior art, the above-mentioned technical solution provided in this application has the following advantages: The method provided in this application divides the image to be displayed into multiple sub-images corresponding to the backlight partition and performs partition brightness statistics. Combined with three-level progressive image enhancement processing of dark areas, sharpness and motion, it can perform fine optimization on the dark areas, sharpness areas and motion areas of each sub-image respectively. It effectively improves the problems of loss of dark details, edge blur and motion blur distortion caused by the global uniform enhancement of the prior art. At the same time, it performs step-by-step correction based on the initial partition brightness data, so that the partition backlight brightness and the image enhancement effect are accurately matched, significantly improving the image display layering, clarity and dynamic display quality. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0020] Figure 1 A schematic flowchart of an image enhancement method provided in an embodiment of this application; Figure 2 This is a schematic diagram of a partitioned LED array for an image playback device provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the process of image enhancement and fine-synchronized adjustment of zone brightness in this application; Figure 4 A flowchart illustrating a method for determining correction parameters for dark area enhancement processing provided in an embodiment of this application; Figure 5 A flowchart illustrating a method for determining sharpness enhancement processing correction parameters provided in an embodiment of this application; Figure 6 A flowchart illustrating a method for determining motion enhancement processing correction parameters provided in an embodiment of this application; Figure 7 A flowchart illustrating the steps of an image enhancement method provided in this application embodiment; Figure 8 This is a schematic diagram of the structure of an image enhancement device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0023] To address the shortcomings of existing technologies in providing differentiated and refined processing for dark areas, edge details, and moving regions, which can lead to loss of dark details, blurred edges, or motion blur; the discrepancy between the luminous intensity of the localized LED light source and the actual brightness of the displayed image's localized areas, resulting in insufficient matching and precision between image display and light control, and impairing image contrast and quality; and the lag between localized brightness data and image enhancement processing, causing a delay in the adjustment of localized LED light source brightness and resulting in asynchrony between image display and localized LED brightness adjustment, thus producing a trailing phenomenon and affecting image display quality, this application provides an image enhancement... The invention relates to a powerful method, apparatus, electronic device, and storage medium. By dividing the image to be displayed into multiple sub-images corresponding to backlight zones and performing brightness statistics for each zone, and combining this with a three-level progressive image enhancement processing of dark areas, sharpness, and motion, it can perform fine-grained optimization for the dark areas, sharpness areas, and motion areas of each sub-image. This effectively improves the problems of lost details in dark areas, blurred edges, and motion blur distortion caused by global uniform enhancement in existing technologies. At the same time, it performs progressive corrections based on the initial zone brightness data, so that the zone backlight brightness and image enhancement effect are accurately matched, significantly improving the sense of layering, clarity, and dynamic display quality of the image.

[0024] Figure 1 This is a flowchart illustrating an image enhancement method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method specifically includes: S101. Receive the input signal from the image playback device and perform decoding processing on the input signal to obtain the image to be displayed.

[0025] In this embodiment, input signals transmitted from image playback devices (such as TVs, monitors, projectors, etc.) can be received through various interfaces such as HDMI, DP, and USB. The input signals can be encoded video streams or image data.

[0026] Then, according to the encoding format of the input signal (such as H.264, H.265, AVS, etc.), the corresponding decoding operation is performed to restore the compressed signal to the original digital image data, that is, the image to be displayed. The image to be displayed usually contains color space information such as RGB or YUV of the pixels.

[0027] S102. Divide the image to be displayed into multiple sub-images that correspond one-to-one with the backlight partitions of the image playback device according to the preset partitioning rules, and obtain a set of sub-images.

[0028] In this embodiment, the preset partitioning rules can be pre-set according to the backlight hardware structure of the image playback device. For example, according to the physical arrangement of the backlight partitions (such as a matrix arrangement of M rows horizontally and N columns vertically), the image to be displayed is evenly divided into M×N sub-images that correspond one-to-one with the size and position of the backlight partitions.

[0029] For example, if the backlight system of an image playback device contains 100 zones (10 rows × 10 columns), the image to be displayed is also divided into 100 sub-images of 10 rows × 10 columns. The pixel range of each sub-image corresponds exactly to the display area covered by a backlight zone, ensuring that the brightness information of the sub-image can directly reflect the display requirements of the corresponding backlight zone. After the division is completed, all sub-images together constitute a sub-image set, laying the foundation for subsequent zone brightness statistics and enhancement processing.

[0030] like Figure 2 As shown, the entire LED light source is divided into N partition arrays; the video image is also divided into N regions corresponding to the LED partition arrays.

[0031] S103. Perform initial brightness statistics of each sub-image in the sub-image set in parallel to obtain an initial partition brightness data table including the original brightness information of each backlight partition.

[0032] In this embodiment, to improve processing efficiency, a parallel computing method is used to synchronously count the brightness information of each sub-image.

[0033] Specifically, for each sub-image, all pixels within it are traversed, and the brightness value of each pixel is extracted. Then, statistical parameters of the brightness of all pixels within each sub-image are calculated, including at least the average brightness value, the maximum brightness value, and the minimum brightness value. These statistical parameters are associated with the corresponding backlight zone identifiers and integrated to form an initial zone brightness data table. This data table clearly records the original brightness characteristics of the sub-image corresponding to each backlight zone, providing basic data support for subsequent image enhancement processing and backlight brightness correction.

[0034] S104. Based on the initial partition brightness data table, obtain the partition correction data generated by each sub-image in the sub-image set during the image enhancement process, and obtain a partition brightness correction data table including the brightness information of each backlight partition after correction.

[0035] In this embodiment, based on the initial partition brightness data table obtained in advance, for each sub-image in the sub-image set, during the image enhancement processing such as shadow enhancement, sharpness enhancement and motion enhancement, the corresponding partition correction data is collected and extracted. Then, these partition correction data are associated and integrated with each backlight partition to finally form a partition brightness correction data table containing the brightness information of all backlight partitions after correction, providing a data basis for subsequent precise control of backlight brightness.

[0036] S105. Control the LED array brightness of the corresponding backlight zone of the image playback device according to the partition brightness correction data table to achieve image enhancement.

[0037] In this embodiment, an LED driving signal corresponding to each backlight zone is generated based on the corrected brightness information of each backlight zone recorded in the partition brightness correction data table. The driving signal contains the target luminous brightness parameter. The driving signal is sent to the backlight control unit of the image playback device. The backlight control unit adjusts the LED array current or voltage of the corresponding backlight zone according to the driving signal, so that the actual luminous brightness of the LED array is consistent with the target brightness in the partition brightness correction data table. Thus, by combining the dynamic adjustment of backlight brightness with image enhancement processing, the overall optimization of the brightness, contrast and detail of the display screen is achieved.

[0038] The image enhancement method provided in this application divides the image to be displayed into sub-images corresponding to backlight zones one by one, and performs parallel partition brightness statistics based on the initial brightness information of each sub-image, which can accurately capture the brightness characteristics of the display content corresponding to each backlight zone. On this basis, by acquiring the partition correction data generated by each sub-image in the dark area, sharpness and motion enhancement processing, a partition brightness correction data table is formed, which enables the control module to accurately control the luminous brightness of the LED array of the corresponding backlight zone of the image playback device according to the table; it realizes deep coupling and precise matching between image content enhancement and backlight brightness adjustment, effectively solving the problems of local detail loss, edge blurring and motion blur caused by global processing in traditional image enhancement methods, while ensuring that the adjustment of backlight brightness can respond to changes in image content in real time, significantly improving the dynamic range, sense of layering and overall visual quality of image display.

[0039] In an optional embodiment of the present invention, the step of obtaining partition correction data generated by each sub-image in the sub-image set during image enhancement processing based on the initial partition brightness data table, to obtain a partition brightness correction data table including brightness information of each backlight partition after correction, includes: Determine the correction parameters corresponding to each sub-image in each level of enhancement processing, and perform multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters. The correction parameters include shadow correction value, sharpness correction value and motion correction value, and the multi-level image enhancement processing includes shadow enhancement processing, sharpness enhancement processing and motion enhancement processing. Based on the initial partition brightness data table, synchronous step-by-step corrections are made based on the dark area correction value, sharpness correction value, and motion correction value of each sub-image to obtain a partition brightness correction data table that matches the enhanced image.

[0040] In this embodiment, firstly, for each level of image enhancement processing, the corresponding correction parameters are determined. After determining the correction parameters for each level, the processing is performed on each sub-image in the order of shadow enhancement, sharpness enhancement, and motion enhancement.

[0041] Specifically, in the shadow enhancement stage, the brightness of pixels in shadow areas below a set brightness threshold is adjusted point by point using shadow correction values, and shadow details are stretched to the visible brightness range through a non-linear mapping algorithm. After entering the sharpness enhancement stage, edge detection is performed on the sub-image after shadow enhancement based on the sharpness correction values, and directional sharpening filters are applied to the detected edge pixels to enhance the brightness contrast on both sides of the edge and improve the image's outline clarity. Finally, in the motion enhancement stage, the pixels in the identified moving areas are dynamically compensated according to the motion correction values. By using frame interpolation compensation or motion trajectory brightness weighting, the brightness loss during motion is made up for, and motion blur is reduced.

[0042] It should be noted that while performing the above multi-level image enhancement processing, synchronous step-by-step corrections are performed based on the initial partition brightness data table. Specifically, after the shadow enhancement processing is completed, the brightness information of the corresponding sub-image in the initial partition brightness data table is corrected at the first level according to the shadow correction value to obtain the partition brightness data after shadow correction; after the sharpness enhancement processing is completed, the partition brightness data after shadow correction is corrected in combination with the sharpness correction value to consider the impact of sharpening processing on the overall brightness of the area; after the motion enhancement processing is completed, the data after the first two levels of correction are corrected at the third level according to the motion correction value, finally forming a partition brightness correction data table that accurately matches the content of the enhanced image, ensuring that the data table can accurately reflect the target brightness required by each backlight partition after the three-level enhancement processing.

[0043] like Figure 3 As shown, the image enhancement processing provided in this embodiment of the invention mainly includes two parallel and deeply interconnected processes: image enhancement processing and partition brightness correction.

[0044] The input image is sent to the image enhancement module and the partition brightness calculation module, respectively. The image enhancement module performs three levels of processing in sequence: shadow enhancement, sharpness enhancement, and motion enhancement, generating shadow correction values, sharpness correction values, and motion correction values ​​respectively. The partition brightness calculation module first counts the image partition brightness data and generates an initial partition brightness data table. Based on the initial data table mentioned above, the partition brightness correction module receives three types of correction values ​​in sequence: dark areas, sharpness, and motion. It then corrects the partition brightness data step by step, ultimately generating a partition brightness correction data table that precisely matches the image enhancement effect. This data table is used to control the backlight partition LED array brightness of the image playback device.

[0045] Figure 4 This is a flowchart illustrating a method for determining correction parameters in dark area enhancement processing, as provided in an embodiment of this application. Figure 4 As shown, the correction parameters for each sub-image in the shadow enhancement process include: S401. Sequentially determine whether there are dark areas in each sub-image.

[0046] Dark areas refer to regions in a sub-image where the pixel brightness value is lower than a preset dark area threshold. This dark area threshold can be set according to the dynamic range of the image and the characteristics of human visual perception, for example, it can be set to 15% of the maximum brightness value of the image.

[0047] In this embodiment, each pixel of the sub-image is traversed, and the pixel brightness value is compared with the dark area threshold. If there are consecutive or discrete pixels with brightness lower than the threshold, it is determined that the sub-image has a dark area.

[0048] S402. For any sub-image containing a dark area, determine the maximum set brightness value for the dark area.

[0049] In this embodiment, the average brightness value and minimum brightness value in the initial partition brightness data of the sub-image are first obtained. Combined with the range of human eye perception of dark details (it is generally believed that the effective display brightness range of dark details is 1.8 times the minimum brightness value to the average brightness value), the maximum set brightness value of the dark area is calculated.

[0050] For example, if the average brightness of a sub-image is 20 nits and the minimum brightness is 5 nits, then the maximum brightness setting can be set to 20 × 1.8 = 36 nits, ensuring that the details in the dark areas are enhanced without exceeding the range of human eye comfort perception.

[0051] S403. Based on the maximum set brightness value, determine the target pixel brightness of the dark area.

[0052] In this embodiment, after determining the maximum set brightness value for the dark area, the target pixel brightness for the dark area is determined based on this value. Specifically, the original pixel brightness of the dark area is used as a benchmark, combined with the preset maximum set brightness value, to calculate the brightness enhancement ratio for that area. At the same time, based on the original brightness distribution characteristics of the dark area (such as average brightness and minimum brightness), a uniform brightness enhancement coefficient adapted to the area is determined with the maximum set brightness value as the upper limit, ensuring that the brightness enhancement can fully highlight the details in the dark area without exceeding the range of human eye comfort.

[0053] Specifically, after successfully identifying the dark areas in the image, a histogram equalization algorithm S is used to automatically enhance the pixel values ​​in those areas. After the dark areas are enhanced, the brightness value of the pixels in the area is adjusted to S*Lmax (where S is the enhancement coefficient of the histogram equalization algorithm on the pixel value, and Lmax is the maximum brightness value), thereby effectively improving the visual effect of the dark areas, making the originally dim and blurry parts clearly visible, and improving the overall image quality and viewing experience.

[0054] S404. Determine the dark correction value of the sub-image based on the original pixel brightness before the dark area enhancement and the target pixel brightness.

[0055] The shadow correction value is the difference between the target pixel brightness and the original pixel brightness.

[0056] In this embodiment, for each pixel in the dark area, the original brightness value of the pixel is subtracted from the determined target pixel brightness to obtain the brightness correction amount of a single pixel; then, taking the dark area of ​​the sub-image as a unit, the average value of the brightness correction amounts of all pixels is calculated as the corresponding dark correction value of the sub-image in the dark area enhancement processing.

[0057] For example, if the original brightness of a pixel in a dark area is 8 nits and the target pixel brightness is 25 nits, then the brightness correction for that pixel is 17 nits; if the average brightness correction for all pixels in that dark area is 15 nits, then the dark area correction value for that sub-image is 15 nits. This dark area correction value will be used to perform the first-level correction on the brightness information of the corresponding sub-image in the initial partition brightness data table, to reflect the adjustment of the backlight partition brightness requirements by the dark area enhancement processing.

[0058] The method for determining dark area enhancement processing correction parameters provided in this application first identifies dark areas in a sub-image, then determines the maximum set brightness value for the dark areas based on the characteristics of human visual perception, and calculates the target pixel brightness accordingly. Finally, the average value of the pixel brightness correction within the area is used as the dark area correction value, achieving precise quantification of the dark area enhancement requirements. This not only effectively avoids image distortion problems caused by noise amplification or excessive brightness enhancement that may occur in traditional dark area enhancement, but also ensures that the dark area correction value is highly matched with the actual brightness characteristics of the sub-image. This provides accurate and reliable basic parameters for the subsequent step-by-step correction of the partition brightness data table, enabling the brightness adjustment of the backlight partition to better cooperate with the dark area enhancement processing, significantly improving the clarity and visual appeal of dark details in the image.

[0059] In an optional embodiment of the present invention, determining whether a dark region exists in a sub-image includes: Calculate the average pixel brightness of all pixels in the sub-image; if the average pixel brightness is less than the dark area determination brightness threshold, determine that there is a dark area in the sub-image; if the average pixel brightness is greater than or equal to the dark area determination brightness threshold, determine that there is no dark area in the sub-image.

[0060] The brightness threshold for dark areas can be set based on the overall dynamic range of the image and the characteristics of the display device.

[0061] In this embodiment, the average pixel brightness is obtained by calculating the arithmetic mean of the brightness values ​​of all pixels in the sub-image. When the calculated average pixel brightness is less than the dark area determination brightness threshold, the sub-image is considered to be dark overall and there are dark areas that need to be enhanced. Conversely, if the average pixel brightness is greater than or equal to the threshold, the sub-image is determined to not have significant dark areas and no dark area enhancement processing is required.

[0062] By using the above-mentioned method based on the overall average brightness of the sub-image, the areas that need to be enhanced in the dark can be quickly and effectively screened out, providing a clear processing target for the subsequent calculation of dark correction values. At the same time, it simplifies the computational complexity of dark area identification and helps to improve the processing efficiency of the entire image enhancement process.

[0063] For example, the process of determining the correction parameters for dark area enhancement uses an adaptive histogram equalization algorithm to improve the brightness and contrast of dark areas of the image and avoid blurring in dark scenes.

[0064] Specifically, firstly, a luminance value Lid is set to determine the dark area, and a maximum luminance value Lmax is set to enhance it. If the average luminance value Lavg of all pixels in a certain area of ​​the image is less than the luminance value Lid, then that area is marked as a dark area. After identifying the dark area, the pixel values ​​in the dark area are automatically enhanced using a histogram equalization algorithm S, and the luminance value of the enhanced pixel is S*Lmax. If there is no dark area, no processing is performed. Then, the original pixel luminance value of the dark area is set to DPo, and the dark area correction value is S*Lmax-DPo. After completing the image enhancement processing of the dark area, the dark area correction value is synchronously output to the dark area luminance correction unit of the partition luminance correction module.

[0065] Figure 5 This is a flowchart illustrating a method for determining sharpness enhancement processing correction parameters provided in an embodiment of this application, as shown below. Figure 5 As shown, the correction parameters for each sub-image in the sharpness enhancement process include: S501. For any sub-image, perform grayscale processing on the sub-image, and perform Fourier transform on the grayscale image to obtain the image spectrum function of the sub-image.

[0066] In this embodiment, the sub-image is first converted from the RGB color space to a grayscale image. The grayscale value of each pixel is calculated using a weighted average method, with the formula: Gray = 0.299×R + 0.587×G + 0.114×B, where R, G, and B are the red, green, and blue channel values ​​of the pixel, respectively, to eliminate color interference, highlight the brightness distribution characteristics of the image, and lay the foundation for subsequent spectrum analysis. Then, a two-dimensional Fourier transform is performed on the grayscale sub-image to convert the image from the spatial domain to the frequency domain, obtaining an image spectrum function containing low-frequency components (corresponding to smooth areas) and high-frequency components (corresponding to edge, texture, and other detail areas). By analyzing the spectrum function, the high-frequency detail areas in the sub-image that need sharpening enhancement can be accurately located.

[0067] S502. The image spectrum function is compared with a preset sharpness cutoff frequency to determine the sharpness region of the sub-image, wherein the frequency of the sharpness region is greater than the preset sharpness cutoff frequency.

[0068] The preset sharpness cutoff frequency is set based on the human eye's perception threshold of image edge sharpness; for example, it can be set to 0.3 times the image sampling frequency.

[0069] In this embodiment, by comparing the amplitude of each frequency component in the image spectrum function with the cutoff frequency, the region with a frequency higher than the cutoff frequency is determined as a sharp region, which includes detailed regions such as image edges and textures that need to be sharpened; while the region with a frequency lower than the cutoff frequency is a smooth region that does not require sharpening processing; thus, it is possible to accurately distinguish between detailed regions and smooth regions in the image, providing a precise range of action for subsequent directional sharpening filtering.

[0070] S503. The spectrum of the sharpness region is enhanced based on a preset sharpening intensity coefficient and a high-pass filter function, and the pixel brightness of the sub-image after sharpening is obtained by inverse Fourier transform.

[0071] In this embodiment, a high-pass filter function that matches the spectral characteristics of the sharpness region is first selected, such as a Gaussian high-pass filter or a Laplace high-pass filter. The standard deviation of the Gaussian high-pass filter can be set according to the frequency distribution characteristics of the sharpness region. For example, for the edge region where high-frequency components are concentrated, the standard deviation can be set to 1.2-1.5.

[0072] Then, a preset sharpening intensity coefficient (which is dynamically adjusted based on the edge complexity of the image content; the higher the edge density, the larger the coefficient value, typically ranging from 0.8 to 1.5) is multiplied by a high-pass filter function to obtain an enhanced high-pass filter operator. This operator is then applied to the spectrum of the sharpening region to amplify the high-frequency components. The amplification factor is positively correlated with the sharpening intensity coefficient to enhance the amplitude of edge details. After completing the spectrum enhancement, a two-dimensional inverse Fourier transform is performed on the processed spectrum to convert it from the frequency domain back to the spatial domain, obtaining the pixel brightness data of the sub-image after sharpening enhancement. At this point, the pixel brightness gradient of the edge region will be significantly improved, and the contour features will be more prominent.

[0073] S504. Determine the sharpness correction value of the sub-image based on the original pixel brightness before sharpness enhancement and the pixel brightness after sharpness enhancement in the sub-image sharpness region.

[0074] In this embodiment, the brightness difference of all pixels in the sharpness region before and after sharpness enhancement is first extracted, that is, the brightness of the pixel after sharpness enhancement minus the original pixel brightness, to obtain the sharpness correction amount of each pixel. Then, taking the sharpness region of the sub-image as the statistical unit, the root mean square value of the sharpness correction amount of all pixels in the region is calculated as the sharpness correction value of the sub-image.

[0075] For example, if the original brightness of a pixel within a certain sharpness area is 120 nits, and the brightness after sharpening is 135 nits, its sharpness correction is 15 nits; if the root mean square value of the sharpness correction for all pixels within that area is 12 nits, then the sharpness correction value for that sub-image is 12 nits. This sharpness correction value will be used to perform a second-level correction on the brightness data of the dark areas after correction, to compensate for the impact of sharpening on the overall brightness of the backlight areas, ensuring that the backlight brightness matches the display requirements of the sharpened image details.

[0076] The sharpness enhancement processing correction parameter determination method provided in this application involves converting a sub-image to grayscale and performing a Fourier transform to obtain the image spectrum function. This accurately locates high-frequency sharpness regions, then enhances the spectrum of these regions using a preset sharpening intensity coefficient and a high-pass filter function. Finally, an inverse Fourier transform is used to obtain the pixel brightness after sharpening, and the root mean square value of the pixel brightness correction within the sharpness region is used as the sharpness correction value, thus achieving a scientific quantification of sharpness enhancement requirements. This frequency domain analysis-based sharpness enhancement parameter determination method can directionally enhance image edges and texture details, avoiding noise amplification and over-sharpening problems that may occur with traditional spatial domain sharpening algorithms. Simultaneously, it ensures that the sharpness correction value accurately reflects the impact of sharpening processing on the brightness of backlight zones, providing a reliable basis for the second-level correction of zone brightness data. This allows the brightness adjustment of backlight zones to better coordinate with sharpness enhancement processing, significantly improving the clarity and layering of image details.

[0077] For example, the sharpness enhancement processing correction parameter determination process employs an adaptive frequency domain enhancement algorithm to achieve image sharpness enhancement and avoid image blurring. First, the image is converted to grayscale, transforming the color image into a grayscale image, and then the image spectrum function is obtained through Fourier transform.

[0078] The sharpness cutoff frequency d, sharpening intensity coefficient g, sharpness scaling coefficient k, and high-pass filter function H are set as follows: If the spectrum of a certain region in the image exceeds the cutoff frequency d, it is marked as a sharp region, and the spectrum of that region is automatically enhanced. The sharpened image spectrum function is FH = F + k*F*H (where F is the original spectrum of the sharp region). If there is no sharp region, no processing is performed. Subsequently, an inverse Fourier transform is used to obtain the sharpened grayscale image, and its enhanced pixel brightness value is FHpix, thus completing the image sharpness enhancement. When the original pixel brightness value of the sharp region is LPo, the sharpness correction value is FHpix - LPo. After the sharpness region image enhancement processing is completed, the sharpness correction value is synchronously output to the sharpness region brightness correction unit of the partition brightness correction module.

[0079] Figure 6 This is a flowchart illustrating a method for determining motion enhancement processing correction parameters provided in an embodiment of this application, as shown below. Figure 6 As shown, the correction parameters for each sub-image in motion enhancement processing include: S601. For any sub-image, identify the motion region in the sub-image based on the pixel difference between the sub-image and the adjacent previous frame image.

[0080] In this embodiment, the absolute value of the brightness difference between the corresponding pixels in the current sub-image and the adjacent previous frame image is first calculated using the inter-frame difference method. A motion determination threshold is set (this threshold is set according to the dynamic response characteristics of the display device and the common motion speed range, such as 3-5 nits). When the absolute value of the brightness difference of a pixel is greater than the threshold, the pixel is determined to be a moving pixel. Then, the 8-neighborhood connected component analysis algorithm is used to cluster consecutive moving pixels into moving regions, while filtering out isolated motion noise regions with an area smaller than the preset number of pixels (such as 10×10 pixels) to accurately identify the region where the real moving object is located in the sub-image.

[0081] S602. Determine the motion enhancement coefficient of the sub-image based on the motion amplitude of the motion region.

[0082] Motion amplitude is characterized by calculating the average displacement of all moving pixels within the motion region.

[0083] In this embodiment, an optical flow estimation algorithm (such as the Lucas-Kanade algorithm) is used to track feature points within the moving region, obtain the coordinate offset of corresponding feature points in two adjacent frames, and then calculate the displacement vector of each moving pixel. The arithmetic mean of the displacement vectors of all moving pixels is calculated to obtain the average motion amplitude of the moving region. Based on the average motion amplitude and a preset motion level classification standard, a corresponding motion enhancement coefficient is matched. The magnitude of the motion enhancement coefficient directly determines the intensity of subsequent motion compensation, ensuring that appropriate enhancement processing is applied to regions with different motion speeds.

[0084] S603. Based on the motion enhancement coefficient, the pixel brightness of the motion region is compensated and adjusted to obtain the pixel brightness after motion enhancement.

[0085] In this embodiment, a dynamic compensation algorithm is used to adjust the pixel brightness within the moving region based on its motion direction and motion enhancement coefficient. Specifically, for each pixel in the moving region, its corresponding pixel position in adjacent frames is determined based on its displacement vector. The pixel brightness of the current frame is then weighted and superimposed with the corresponding pixel brightness of the previous frame, with the weights dynamically allocated by the motion enhancement coefficient. For example, when the motion enhancement coefficient is 1.2, the weight of the current frame pixel brightness is set to 0.7, and the weight of the corresponding pixel brightness of the previous frame is set to 0.3. The superimposed brightness value is the pixel brightness after motion enhancement. For fast-moving regions, the weight of the current frame pixel brightness is appropriately increased to reduce motion blur; for slow-moving regions, the weight of the current frame is decreased to enhance the continuity of the image. Through this dynamic brightness compensation based on motion amplitude and direction, the clarity and dynamic response speed of the moving region are effectively improved.

[0086] S604. Determine the motion correction value of the sub-image based on the original pixel brightness before motion enhancement and the pixel brightness after motion enhancement of the sub-image motion region.

[0087] In this embodiment, the brightness difference of all pixels in the motion region before and after motion enhancement is first extracted, that is, the brightness of the pixel after motion enhancement minus the original pixel brightness, to obtain the motion correction amount of each pixel.

[0088] It should be noted that, considering the potential brightness fluctuations in moving areas, a weighted average method is used to calculate the motion correction value to avoid the influence of individual abnormal pixels on the correction value. The weight is positively correlated with the pixel's motion amplitude; that is, pixels with larger motion amplitudes have a higher weight in the correction value calculation. For example, if a pixel in a moving area has a motion amplitude of 5 pixels / frame, its motion correction is 8 nits, and its weight coefficient is set to 1.2; another pixel has a motion amplitude of 2 pixels / frame, a correction of 3 nits, and a weight coefficient of 0.8. Then, the motion correction value for this area is (8 × 1.2 + 3 × 0.8) / (1.2 + 0.8) = 6.6 nits. This motion correction value will be used to perform a third-level correction on the partition brightness data after dark and sharpness corrections to compensate for the impact of motion enhancement processing on the dynamic brightness of the backlight partitions. This ensures that the backlight brightness can match the display requirements of moving images in real time, effectively reducing motion blur and ghosting, and improving the smoothness and clarity of dynamic images.

[0089] The motion enhancement processing correction parameter determination method provided in this application identifies motion regions based on inter-frame pixel differences, determines motion amplitude using an optical flow estimation algorithm to match the motion enhancement coefficient, dynamically compensates and adjusts pixel brightness in the motion region according to the motion direction and enhancement coefficient, and finally calculates the motion correction value using a motion amplitude-weighted average, achieving precise quantification of motion enhancement requirements. This parameter determination method based on motion region characteristic analysis can apply differentiated enhancement processing to regions with different motion speeds and directions, avoiding static region artifacts that may be caused by traditional global motion compensation. It also ensures that the motion correction value accurately reflects the impact of motion enhancement on the dynamic brightness of backlight zones, providing a scientific basis for the third-level correction of zone brightness data. This allows the brightness adjustment of backlight zones to adapt to changes in moving images in real time, significantly improving the clarity, smoothness, and viewing comfort of dynamic images.

[0090] For example, the motion enhancement processing correction parameters are determined using a multi-frame neighborhood averaging method to achieve motion image enhancement and avoid motion jitter. Specifically, the pixel matrices Cfn, Cfn+1, and Cfn+2 of three adjacent frames in the video are first obtained, where Cfn is the pixel matrix of the current image frame, and Cfn+1 and Cfn+2 are the pixel matrices of the subsequent image frames to be displayed.

[0091] Calculate the inter-frame pixel difference matrix Df1=Cfn-Cfn+1 and Df2=Cfn+1-Cfn+2; set a judgment value Jd. If the number of non-zero pixels in the pixel difference matrix Df1 or Df2 is less than Jd, it is determined to be a moving region and motion enhancement processing is performed; otherwise, no processing is performed.

[0092] The specific operation of motion enhancement processing is as follows: the pixel matrix of the adjacent image frames of the current frame is adjusted to DCfn+1=Cfn+1+y1*Df1+y2*Df2 (y1 and y2 are preset coefficients); that is, the pixels of the next frame of the current frame are enhanced, and the brightness increment of the enhanced pixels is RSPo=DCfn+1-Cfn+1, thereby achieving motion enhancement and avoiding motion jitter caused by the brightness difference between adjacent frames. If the original pixel brightness value of the motion region is SPo, then the motion correction value is RSPo-SPo; ​​after completing the image enhancement processing of the motion region, the motion correction value is synchronously output to the motion region brightness correction unit of the partition brightness correction module.

[0093] In an optional embodiment of the present invention, the step of sequentially performing multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters includes: For any sub-image, extract the shadow correction value corresponding to shadow enhancement processing from the correction parameters, and perform shadow enhancement processing on the shadow areas of the sub-image based on the shadow correction value to optimize the shadow brightness of the sub-image; after the shadow enhancement processing is completed, extract the sharpness correction value corresponding to sharpness enhancement processing from the correction parameters, and perform sharpness enhancement processing on the sharp areas of the sub-image based on the sharpness correction value to improve the edge clarity of the sub-image; after the sharpness enhancement processing is completed, extract the motion correction value corresponding to motion enhancement processing from the correction parameters, and perform motion enhancement processing on the moving areas of the sub-image based on the motion correction value to compensate for the brightness loss in the moving areas.

[0094] In this embodiment, shadow enhancement improves the brightness of dark areas, revealing details hidden in shadows while avoiding excessive impact on bright areas, ensuring overall image contrast balance. Following shadow enhancement, sharpness enhancement targets edges and textures, enhancing high-frequency components to make object outlines clearer and details richer, effectively improving image clarity and sharpness. Motion enhancement focuses on moving areas, adjusting brightness of moving pixels using dynamic compensation algorithms to reduce motion blur and ghosting caused by speed differences, significantly improving dynamic clarity and smoothness, especially in fast-moving scenes. These three enhancement processes work sequentially, forming a cohesive whole. Shadow enhancement provides a better image foundation for subsequent sharpness and motion enhancement, sharpness enhancement optimizes details without interfering with the dynamic characteristics of moving areas, and motion enhancement further enhances the visual appeal of dynamic scenes based on shadow and sharpness optimization. The three work synergistically to achieve a comprehensive improvement in image quality.

[0095] Figure 7A flowchart illustrating the steps of an image enhancement method provided in this application embodiment is shown below. Figure 7 As shown, the implementation process of the image enhancement method mainly includes the following steps: Step 1: The video decoding module receives the input signal, decodes it, and outputs a video image.

[0096] Step 2: Simultaneously perform zone brightness calculation and image enhancement processing: 1) The partition brightness calculation module sets the number of partitions to N, divides the image into N regions, calculates the brightness data LumK (K=1, 2, ..., N) for each partition, and outputs the partition brightness data table to the partition brightness correction module; 2) The image enhancement module includes processing units such as dark area enhancement, sharpness enhancement, and motion enhancement. After the image enhancement process is completed, the enhanced image is output to the display driver control module. At the same time, the enhanced brightness correction value is output to the partition brightness correction module (e.g., the corrected brightness value LcpK (K=1, 2, ..., N) for partition K). If the brightness of partition K does not need to be corrected, this value is not output.

[0097] Step 3: Perform partition brightness correction processing: The partition brightness correction module receives the partition brightness data table; if a brightness correction value is also received, the brightness data of the corresponding partition in the table is corrected - for example, the original brightness data of partition K is LumK, after receiving LcpK, the corrected brightness data is LumK+LcpK; finally, the corrected partition brightness data correction table is output.

[0098] Step 4: Image-driven display and zoned LED array illumination: The display driver control module converts and processes the enhanced image to generate a screen drive signal to drive the screen display; at the same time, the zone drive control module obtains the brightness data of each zone in the zone brightness data correction table, and synchronously controls and adjusts the illumination of the corresponding zone's LED array to provide a light source for the screen display and complete the image display.

[0099] Through the collaborative processing of steps two through four, the changes in zone brightness can be acquired and the corresponding data corrected in real time while the image is enhanced. This ensures that the image enhancement display and the zone LED brightness adjustment are synchronized, avoiding delays and inaccuracies in brightness adjustment, achieving dynamic light control, and improving display quality.

[0100] This invention proposes an image enhancement method, executed by a system comprising a video decoding module, an image enhancement module, a zone brightness calculation module, a zone brightness correction module, a display driver control module, a zone driver control module, a screen, and a zone LED array. The image enhancement module includes processing units for dark area enhancement, sharpness enhancement, and motion enhancement, while the zone brightness correction module has functional units for dark area brightness correction, sharpness area brightness correction, and motion area brightness correction. The video decoding module decodes the input signal and outputs a video image, then simultaneously initiates zone brightness calculation and image enhancement processing: the zone brightness calculation module divides the image into N regions, calculates the brightness data for each region, and outputs it to the zone brightness correction module; the image enhancement module sequentially performs dark area enhancement, sharpness enhancement, and motion enhancement on the video image, outputting the enhanced image while simultaneously transmitting dark area correction values, sharpness correction values, and motion correction values ​​to the zone brightness correction module. The zone brightness correction module receives the zone brightness data table, simultaneously acquires the aforementioned correction values ​​during the image enhancement process, and sequentially corrects the brightness of dark areas, sharpness areas, and motion areas in the zone brightness data table, finally outputting a zone brightness data correction table. Subsequently, the display driver control module converts the enhanced image into a screen drive signal to drive the screen display, while the zone driver control module synchronously reads the brightness data of each zone in the zone brightness data correction table, adjusts the light emission state of the corresponding zone LED array, provides a light source for the screen, and completes the image display. This invention achieves synchronization between image enhancement display and zone LED brightness adjustment, effectively avoiding the problems of delay and inaccuracy in brightness adjustment, and significantly improves the display quality through dynamic light control.

[0101] Furthermore, in this embodiment, an initial partition brightness data table is pre-calculated before the image enhancement module is started. As each processing unit of the image enhancement module sequentially performs enhancement operations, the brightness correction value of each individual unit is obtained in real time, and the partition brightness data table is updated synchronously. This design ensures that image enhancement processing and partition brightness data correction are synchronized, guaranteeing a precise match between image display and partition LED light source brightness adjustment. It solves the problems of insufficient matching and poor synchronization between image display and light control adjustment in traditional solutions, significantly improving image display quality and demonstrating broad application prospects.

[0102] Figure 8 This is a schematic diagram of the structure of an image enhancement device provided in an embodiment of this application, as shown below. Figure 8 As shown, the device specifically includes: The receiving module 801 is used to receive the input signal from the image playback device and perform decoding processing on the input signal to obtain the image to be displayed; The partitioning module 802 is used to divide the image to be displayed into multiple sub-images that correspond one-to-one with the backlight partitions of the image playback device according to a preset partitioning rule, thereby obtaining a set of sub-images. The statistics module 803 is used to perform initial brightness statistics of each sub-image in the sub-image set in parallel to obtain an initial partition brightness data table including the original brightness information of each backlight partition; The acquisition module 804 is used to acquire the partition correction data generated by each sub-image in the sub-image set during the image enhancement process based on the initial partition brightness data table, and obtain a partition brightness correction data table including the brightness information of each backlight partition after correction. The control module 805 is used to control the light emission brightness of the LED array of the corresponding backlight zone of the image playback device according to the partition brightness correction data table, so as to achieve image enhancement.

[0103] In one possible implementation, the acquisition module 804 is further configured to determine the correction parameters corresponding to each sub-image in each level of enhancement processing, and perform multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters. The correction parameters include shadow correction values, sharpness correction values, and motion correction values, and the multi-level image enhancement processing includes shadow enhancement processing, sharpness enhancement processing, and motion enhancement processing. Based on the initial partition brightness data table, synchronous step-by-step correction is performed based on the shadow correction values, sharpness correction values, and motion correction values ​​of each sub-image to obtain a partition brightness correction data table that matches the enhanced image.

[0104] In one possible implementation, the acquisition module 804 is further configured to sequentially determine whether there is a dark area in each sub-image; for any sub-image with a dark area, determine the maximum set brightness value of the dark area; based on the maximum set brightness value, determine the target pixel brightness of the dark area; and determine the dark correction value of the sub-image according to the original pixel brightness of the dark area before enhancement and the target pixel brightness.

[0105] In one possible implementation, the acquisition module 804 is further configured to calculate the average pixel brightness of all pixels in the sub-image; if the average pixel brightness is less than the dark area determination brightness threshold, determine that there is a dark area in the sub-image; if the average pixel brightness is greater than or equal to the dark area determination brightness threshold, determine that there is no dark area in the sub-image.

[0106] In one possible implementation, the acquisition module 804 is further configured to: perform grayscale processing on any sub-image; perform Fourier transform on the grayscale image to obtain the image spectrum function of the sub-image; compare the image spectrum function with a preset sharpness cutoff frequency to determine the sharpness region of the sub-image, wherein the frequency of the sharpness region is greater than the preset sharpness cutoff frequency; enhance the spectrum of the sharpness region based on a preset sharpening intensity coefficient and a high-pass filter function, and obtain the pixel brightness of the sub-image after sharpening enhancement through inverse Fourier transform; and determine the sharpness correction value of the sub-image based on the original pixel brightness before sharpening and the pixel brightness after sharpening of the sub-image sharpness region.

[0107] In one possible implementation, the acquisition module 804 is further configured to, for any sub-image, identify a moving region in the sub-image based on the pixel difference between the sub-image and the adjacent previous frame image; determine a motion enhancement coefficient of the sub-image based on the motion amplitude of the moving region; compensate and adjust the pixel brightness of the moving region based on the motion enhancement coefficient to obtain the pixel brightness after motion enhancement; and determine a motion correction value of the sub-image based on the original pixel brightness of the moving region before enhancement and the pixel brightness after motion enhancement.

[0108] In one possible implementation, the acquisition module 804 is further configured to sequentially perform multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters, including: for any sub-image, extracting the dark correction value corresponding to the dark enhancement processing in the correction parameters, performing dark enhancement processing on the dark area of ​​the sub-image based on the dark correction value to complete the dark brightness optimization of the sub-image; after the dark enhancement processing is completed, extracting the sharpness correction value corresponding to the sharpness enhancement processing in the correction parameters, performing sharpness enhancement processing on the sharpness area of ​​the sub-image based on the sharpness correction value to improve the edge clarity of the sub-image; after the sharpness enhancement processing is completed, extracting the motion correction value corresponding to the motion enhancement processing in the correction parameters, performing motion enhancement processing on the motion area of ​​the sub-image based on the motion correction value to compensate for the brightness loss of the motion area.

[0109] The image enhancement device provided in this embodiment can be as follows: Figure 8 The image enhancement device shown can perform the following: Figure 1-7 All steps of image enhancement, thereby achieving Figure 1-7 For details on the image enhancement techniques shown, please refer to [link / reference]. Figure 1-7 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0110] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 9 As shown, this application embodiment provides an electronic device, including a processor 901, a communication interface 902, a memory 903, and a communication bus 904. The processor 901, communication interface 902, and memory 903 communicate with each other via the communication bus 904. The memory 903 stores computer programs. When the processor 901 executes the program stored in the memory 903, it implements the steps of the image enhancement method provided in any of the aforementioned method embodiments. The system receives an input signal from an image playback device and performs decoding processing on the input signal to obtain an image to be displayed. It then divides the image to be displayed into multiple sub-images corresponding one-to-one with the backlight partitions of the image playback device according to a preset partitioning rule, obtaining a set of sub-images. Simultaneously, it performs initial brightness statistics on each sub-image in the sub-image set to obtain an initial partition brightness data table including the original brightness information of each backlight partition. Based on the initial partition brightness data table, it obtains partition correction data generated during image enhancement processing for each sub-image in the sub-image set, obtaining a partition brightness correction data table including the corrected brightness information of each backlight partition. Finally, it controls the LED array illumination brightness of the corresponding backlight partition of the image playback device according to the partition brightness correction data table to achieve image enhancement.

[0112] In one possible implementation, correction parameters corresponding to each sub-image in each level of enhancement processing are determined. Based on the correction parameters, multi-level image enhancement processing is sequentially performed on each sub-image in the sub-image set. The correction parameters include shadow correction values, sharpness correction values, and motion correction values. The multi-level image enhancement processing includes shadow enhancement processing, sharpness enhancement processing, and motion enhancement processing. Using the initial partition brightness data table as a reference, synchronous step-by-step corrections are performed based on the shadow correction values, sharpness correction values, and motion correction values ​​of each sub-image to obtain a partition brightness correction data table that matches the enhanced image.

[0113] In one possible implementation, it is determined sequentially whether there is a dark area in each sub-image; for any sub-image with a dark area, the maximum set brightness value of the dark area is determined; based on the maximum set brightness value, the target pixel brightness of the dark area is determined; and based on the original pixel brightness of the dark area before enhancement and the target pixel brightness, the dark area correction value of the sub-image is determined.

[0114] In one possible implementation, the average pixel brightness of all pixels in the sub-image is calculated; if the average pixel brightness is less than the dark area determination brightness threshold, it is determined that there is a dark area in the sub-image; if the average pixel brightness is greater than or equal to the dark area determination brightness threshold, it is determined that there is no dark area in the sub-image.

[0115] In one possible implementation, for any sub-image, grayscale processing is performed on the sub-image, and a Fourier transform is performed on the grayscale image to obtain the image spectrum function of the sub-image; the image spectrum function is compared with a preset sharpness cutoff frequency to determine the sharpness region of the sub-image, wherein the frequency of the sharpness region is greater than the preset sharpness cutoff frequency; the spectrum of the sharpness region is enhanced based on a preset sharpening intensity coefficient and a high-pass filter function, and the pixel brightness of the sub-image after sharpening is obtained by inverse Fourier transform; the sharpness correction value of the sub-image is determined based on the original pixel brightness before sharpening and the pixel brightness after sharpening of the sub-image sharpness region.

[0116] In one possible implementation, for any sub-image, a motion region in the sub-image is identified based on the pixel difference between the sub-image and the adjacent previous frame image; a motion enhancement coefficient of the sub-image is determined based on the motion amplitude of the motion region; the pixel brightness of the motion region is compensated and adjusted based on the motion enhancement coefficient to obtain the pixel brightness after motion enhancement; and a motion correction value of the sub-image is determined based on the original pixel brightness of the motion region before enhancement and the pixel brightness after motion enhancement.

[0117] In one possible implementation, the step of sequentially performing multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters includes: for any sub-image, extracting the shadow correction value corresponding to shadow enhancement processing from the correction parameters, and performing shadow enhancement processing on the shadow area of ​​the sub-image based on the shadow correction value to optimize the shadow brightness of the sub-image; after the shadow enhancement processing is completed, extracting the sharpness correction value corresponding to sharpness enhancement processing from the correction parameters, and performing sharpness enhancement processing on the sharpness area of ​​the sub-image based on the sharpness correction value to improve the edge clarity of the sub-image; after the sharpness enhancement processing is completed, extracting the motion correction value corresponding to motion enhancement processing from the correction parameters, and performing motion enhancement processing on the motion area of ​​the sub-image based on the motion correction value to compensate for the brightness loss in the motion area.

[0118] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0119] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0120] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An image enhancement method, characterized in that, The method includes: Receive an input signal from an image playback device, and perform decoding processing on the input signal to obtain an image to be displayed; The image to be displayed is divided into multiple sub-images that correspond one-to-one with the backlight partitions of the image playback device according to the preset partitioning rules, thus obtaining a set of sub-images. Perform initial brightness statistics of each sub-image in the sub-image set in parallel to obtain an initial partition brightness data table including the original brightness information of each backlight partition; Based on the initial partition brightness data table, the partition correction data generated by each sub-image in the sub-image set during the image enhancement process is obtained, resulting in a partition brightness correction data table including the brightness information of each backlight partition after correction. The brightness of the LED array in the corresponding backlight zone of the image playback device is controlled according to the partition brightness correction data table to achieve image enhancement.

2. The method according to claim 1, characterized in that, Based on the initial partition brightness data table, the partition correction data generated during the image enhancement process for each sub-image in the sub-image set is obtained, resulting in a partition brightness correction data table including the brightness information of each backlight partition after correction, including: Determine the correction parameters corresponding to each sub-image in each level of enhancement processing, and perform multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters. The correction parameters include shadow correction value, sharpness correction value and motion correction value, and the multi-level image enhancement processing includes shadow enhancement processing, sharpness enhancement processing and motion enhancement processing. Based on the initial partition brightness data table, synchronous step-by-step corrections are made based on the dark area correction value, sharpness correction value, and motion correction value of each sub-image to obtain a partition brightness correction data table that matches the enhanced image.

3. The method according to claim 2, characterized in that, The correction parameters for each sub-image in the shadow enhancement process include: Determine in turn whether there are dark areas in each sub-image; For any sub-image containing dark areas, determine the maximum set brightness value for the dark areas; Based on the maximum set brightness value, determine the target pixel brightness of the dark area; The dark correction value of the sub-image is determined based on the original pixel brightness before the dark area enhancement and the target pixel brightness.

4. The method according to claim 3, characterized in that, Determining whether there are dark areas in a sub-image includes: Calculate the average pixel brightness of all pixels in the sub-image; If the average pixel brightness is less than the dark area determination brightness threshold, it is determined that there is a dark area in the sub-image; If the average pixel brightness is greater than or equal to the dark area determination brightness threshold, it is determined that there is no dark area in the sub-image.

5. The method according to claim 2, characterized in that, The correction parameters for each sub-image in the sharpness enhancement process include: For any sub-image, perform grayscale processing on the sub-image, and perform Fourier transform on the grayscale image to obtain the image spectrum function of the sub-image; The image spectrum function is compared with a preset sharpness cutoff frequency to determine the sharpness region of the sub-image, wherein the frequency of the sharpness region is greater than the preset sharpness cutoff frequency; The spectrum of the sharpness region is enhanced based on a preset sharpening intensity coefficient and a high-pass filter function, and the pixel brightness of the sub-image after sharpening is obtained by inverse Fourier transform. The sharpness correction value of the sub-image is determined based on the original pixel brightness before sharpness enhancement and the pixel brightness after sharpness enhancement in the sub-image sharpness region.

6. The method according to claim 2, characterized in that, The correction parameters for each sub-image in motion enhancement processing include: For any sub-image, based on the pixel difference between the sub-image and the adjacent previous frame image, the motion region in the sub-image is identified; The motion enhancement coefficient of the sub-image is determined based on the motion amplitude of the motion region; The pixel brightness of the moving region is compensated and adjusted based on the motion enhancement coefficient to obtain the motion-enhanced pixel brightness; The motion correction value of the sub-image is determined based on the original pixel brightness before motion enhancement and the pixel brightness after motion enhancement of the sub-image motion region.

7. The method according to claim 2, characterized in that, The step of performing multi-level image enhancement processing on each sub-image in the sub-image set based on the correction parameters includes: For any sub-image, extract the dark correction value corresponding to the dark enhancement processing from the correction parameters, and perform dark enhancement processing on the dark area of ​​the sub-image based on the dark correction value to complete the dark brightness optimization of the sub-image; After the dark area enhancement processing is completed, the sharpness correction value corresponding to the sharpness enhancement processing is extracted from the correction parameters, and the sharpness enhancement processing is performed on the sharpness area of ​​the sub-image based on the sharpness correction value to improve the edge clarity of the sub-image. After the sharpness enhancement process is completed, the motion correction value corresponding to the motion enhancement process is extracted from the correction parameters. Based on the motion correction value, motion enhancement processing is performed on the motion region of the sub-image to compensate for the brightness loss of the motion region.

8. An image enhancement device, characterized in that, The device includes: The receiving module is used to receive input signals from the image playback device and perform decoding processing on the input signals to obtain the image to be displayed; The partitioning module is used to divide the image to be displayed into multiple sub-images that correspond one-to-one with the backlight partitions of the image playback device according to a preset partitioning rule, thereby obtaining a set of sub-images. The statistics module is used to perform initial brightness statistics of each sub-image in the sub-image set in parallel to obtain an initial partition brightness data table including the original brightness information of each backlight partition; The acquisition module is used to acquire the partition correction data generated by each sub-image in the sub-image set during the image enhancement process based on the initial partition brightness data table, and obtain a partition brightness correction data table including the brightness information of each backlight partition after correction. The control module is used to control the brightness of the LED array in the corresponding backlight zone of the image playback device according to the partition brightness correction data table, so as to achieve image enhancement.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the image enhancement method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the image enhancement method according to any one of claims 1 to 7.

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